Hybrid Renewable Power Systems for the Mining Industry: System Costs, Reliability Costs, and Portfolio Cost Risks
Bibliographic record
Abstract
The mineral sector is responsible for more than 38% of total industrial energy use and 11% of total final energy consumption. A rising trend in the industry is the search for cleaner, less carbon-intensive and more efficient energy technologies that can also bring new business opportunities to the industry. Evidence suggests that the inclusion of energy storage and renewables alongside traditional fuel-based power alternatives can both reduce generation costs and carbon emissions in off-grid and distributed power systems. Previous research has quantified this outcome for other industrial and domestic sectors but little investigation has taken place to characterise the potential of hybrid systems in mining settings. The interest of this research is to assess the economic potential of hybrid renewable systems and evaluate the trade-offs associated with the context-dependent factors of the mining industry. An energy optimisation model, named HELiOS-Mining, was developed in order to account for these factors, and search for the least-cost generation alternatives in relation to technical characteristics (i.e. storage strategies, dispatch, demand-shifting, reliability requirements, fuel-mix), economic specificities (i.e. value of lost load, portfolio cost risk, financing), and spatial factors (i.e. access to resources, climate). Three major mining regions are investigated, including: grid-connected and off-grid mining in Northern Chile as well as off-grid mining in North-Western Australia and Yukon, Canada. The results of this research allow important insights to be made into the economics of hybridised power systems in mining settings. Research findings have identified that hybrid renewable power systems can generate life-cycle cost savings of up to 57% and carbon savings of up to 82% (against diesel or grid power baselines). Power systems that feature a renewable penetration of 60 to 85% of total capacity have the lowest costs in three out of four selected mines. Furthermore, portfolio analysis has demonstrated that such power systems can help reducing the cost risk of the industry associated with fuel price variations and carbon policies. Results also illustrate how assumptions about risk factors can drive large shifts in optima, and that concentrated solar power could be a key enabling technology for reducing the emissions of the mining industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".